Section 4 describes the two models we use to tie together reordering and alignments and how we use these models to generate training data for training our reordering model.

these problems are particularly severe when translating between languages which have very different structure.


When we compared two sets of results in Table 4, the joint count version of VSM and linear mixture of TMs, we found that for Arabic to English, VSM performance is better than linear mixture at the p < 0.01 level; the Chinese to English significance test was inconclusive (VSM found to be superior to linear mixture at p < 0.05 for NIST06 but not for NIST08).

a feature derived from this topicrelated vector space might complement some features derived from the subcorpora which we explored in the experiments above, and which seem to exploit information related to genre and style.


Thus, the variant of VSM adaptation tested here bears a superficial resemblance to domain adaptation based on mixture models for TMs, as in (Foster and Kuhn, 2007), in that both approaches rely on information about the subcorpora from which the data originate.

naturally, the conclusions for Arabic don't change.


One can think of several other ways of defining the vector space that might yield even better results than those reported here.

our last set of experiments examined the question: when added to a system that already has some form of linear mixture model adaptation, does VSM improve performance?


This raises the question: is VSM performance significantly better than that of a linear mixture of TMs?

the VSM approach is simpler and more straightforward.


Known disadvantages of this approach include development cost, parsers that contain errors, specification misunderstandings, and specifications that become out of date as the implementation evolves.

this low precision reflects the noisiness of the weak supervision -nearly one half of the parsers produced by No Learning are actually incorrect even though they read all of the input examples without error.


8 The framework repeatedly predicts hidden structures (specification trees in our case) using a structure learner, and trains the structure learner based on the execution feedback of its predictions.

our mechanism, in contrast, automatically generates parser programs from natural language input format descriptions.


There are 991 unique features in total in our experiments.

in our case, we sample one hidden specification tree t i while holding all other trees t −i fixed: where directly solving the subproblem (1) in our case is still hard, we therefore use a Metropolis-Hastings sampler that is similarly applied in traditional sentence parsing problems.


In addition, anaphoric speaker is the least frequent of the three categories.

their results are best compared against our ORACLE approach.


Finally, comparison of various Bayesian models of the same task is rare (Jones and Love, 2011) and Bayesian inference generally can be demonstrated as simply one class of regularization or smoothing techniques among many others; given the problem at hand, there may well be other, equally compelling regularization methods for dealing with the bias-variance dilemma (e.g., SVMs (Shalizi, 2009)).

for comparative purposes, in this paper we examine alternative models for (a) probability estimation and (b) clustering.


This use of a low-frequency threshold for learning has extensive support in the literature for learning of all kinds in both human and non-human animals, e.g.

it remains crucial to carefully assess the use of HBMs along with alternative, possibly simpler, candidate models.


While the models differ somewhat in their predictions, the quantitative differences need to be assessed more carefully.

the gold standard we use for evaluation is the classification predicted by Perfors, Tenenbaum, and Wonnacott (2010).


3 Two-level Context-sensitive Inference Our model follows the general DIRT scheme while extending it to handle context-sensitive scoring of rule applications, addressing the scenario dealt by the context-sensitive topic models.

for example, the inference rule 'X treat Y → X relieve Y' can be useful to extract pairs of drugs and the illnesses which they relieve, or to answer a question like "Which drugs relieve headache?".


(2009) use distributional methods for frame disambiguation.

the final alignment decision is the conjunction of two decision functions: (2) We differ from Niemann and Gurevych 2011in that we use a joint training setup which determines t ppr and t cos to optimize classification performance directly (as proposed in ): where F 1 is the maximized evaluation score and a is the decision function in equation 2.


For each model and training size, we averaged the perplexities from 5 trials of each competition track.

couldn't a system be rewarded simply by being compared against a bad system more frequently than its competitors?


The k-th element of t's vector can be computed with the same method.

through the analysis we find that our induced phrase pairs still contain some noise, such as the words in source-and target-side of the phrase pair are all aligned but the target-side phrase expresses the different meaning.


Token-Level PSD Features These features aim to capture generalized characteristics of a context.

confusionNorm is the ratio of confusion of the prior and posterior distributions, where confusion is defined as earlier.


Confusion is the uncertainty in the most likely prediction given the source token: mediantp (t|s) maxt p(t|s) .

next, we need new-domain monolingual French text (we use medical text, scientific text and movie subtitle text).


New English translations are used as evidence that Latin words have shifted sense.

recall that the micro-level evaluation computes precision, recall, and f-measure for all word tokens of a given word type and then averages across word types.


Sense induction is framed as learning topic distributions for a word type, while disambiguation consists of assigning topics to word tokens.

the type-only features (TYPEONLY)  Table 4: Complete SENSESPOTTING results for all domains.


From this, we extract an old domain sense dictionary, using the Moses MT framework (Koehn et al., 2007).

it is more difficult for our models to make good predictions for less frequent words.


When a solution is not complete, in the computation we include only the sequences of contiguous words (i.e., not interrupted by empty slots) having length greater than or equal to the order of the n-gram model.

4 Concerning the order in which slots are filled, we start from those that have the highest number of dependencies (both head or modifiers) that have been already instantiated in the sentence, i.e., we start from the slots that are connected to the highest number of non-empty slots.


By examining the output, we nd that although the overall performance worsens slightly, the agreement requirement is satised.

it combines two corrections into a single correction.


Since the turkers did not always agree if the type for a sample is good or not, we aggregate their answers.

for a given entity, candidate types are types that can potentially be assigned to that entity, based on the entity's co-occurrences with typed relational patterns.


Prominent examples include Freebase (Bollacker 2008) which powers the Google Knowledge Graph, Con-ceptNet (Havasi 2007), YAGO (Suchanek 2007), and others.

our setting resembles the established task of fine-grained typing for noun phrases (Fleischmann 2002), with the difference being that we disregard common nouns and phrases for prominent in-KB entities and instead exclusively focus on the difficult case of phrases that likely denote new entities.


In the next step we pick the best types for an entity among all its candidate types.

as expected, precision when sampling over all entities drops a bit.


(2006): λ = 0.4, c = 2.4 using the Collins Kernel (Collins and Duffy, 2001).

we will use a binary classifier trained on RE instance representations.


is specified in Figure 3 and P R is explained in the text below.

figure 2 gives the mathematical description of the graphical model and Table 1 provides a key to the variables of our model.


Our experiments are consistent with longstanding and recent findings in linguistics, in particular that /t/-deletion heavily depends on the immediate context and that models ignoring context work poorly on real data.

as they point out, combining the segmentation and the variation model into one joint model is not straight-forward and usual inference procedures are infeasible, which requires the use of several heuristics.


These ratings were averaged item-wise, resulting in a Gaussian distribution with a mean of 3.79 and a standard deviation of 1.31.

the first line of table 5 reports the average quality (on a 7-point scale) of the representations of the derived forms as produced by the models and baseline, as well as of the corpus-harvested ones (corpus column).


Annotation of similarity between stem and derived forms Derived forms differ in terms of how far their meaning is with respect to that of their stem.

it is surprising that a simple representation of bound affixes (i.e., as vectors aggregating the contexts of words containing them) can work so well, at least when used in conjunction with the granular dimension-by-dimension weights assigned by the fulladd method.


It is instructive to reparameterize the Dirichlet with k + 1 parameters: In other words, the parameters α ′ i give the mean of the distribution, and α 0 gives the precision of the distribution.

as Dirichlets lack a standard conjugate prior, we simply use uniform priors over the interval [0, 500].


This gives us an efficient way to compute unnormalized posterior densities for α.

variance across languages is quite low: the standard deviations are about 2 percentage points.


A k−dimensional Dirichlet with parameters α 1 ... α k defines a distribution over the k − 1 simplex with the following density: where α i > 0, θ i > 0, and i θ i = 1.

the mean of these Dirichlets will always be a uniform emission distribution.


Simple English Wikipedia contains more than 60K articles written in simplified English.

for the simplification task, the optimal lambda value determined on the development set was 0.98, with a very strong bias towards the simple model.


For our first task, we evaluated these language models using perplexity based on how well they modeled the simple side of the held-out data.

Figure 6: Kappa rank scores for models trained with varying amounts of simple data combined with increasing amounts of normal data.


sYN Accuracy on string, proportion of shallow verb candidates selected by the system with a string identical to the verb string in the original corpus 4.


The feature templates for REG combine properties of the candidate RE (PoS and lemma for top node and its children, length) with properties of the RE slot in the tree: lemma, PoS and labels for the (i) mother node, (ii) grandmother node, (iii) uncle and sibling nodes.

the REG module applied on prelinSyn −re , predicted shallow and linearized trees, clearly outperforms the module applied on deeplinSyn −re .


Since training and test parts of standard datasets for Arabic NER are drawn from the same genre in relatively close temporal proximity, a named entity recognizer that simply memorizes named entities in the training set generally performs well on such test sets.

we believe that this problem can be overcome by introducing new training data that include tweets (or other social text) and performing domain adaptation.


For both features (using the two translation methods), for an Arabic word sequence corresponding to the DBpedia entry, the first word in the sequence was assigned the feature "B-" plus the DBpedia type and subsequent words were assigned the feature "I-" plus the DBpedia type.

the results may not be completely comparable.


While the full JRC corpus contains roughly 180k word types and consists of approximately 70M running words, the full Gigaword corpus contains around 2M word types and roughly 1.5G running words.

it contains some mistakes and ambiguities: For example, it contains misspelled words like forrest (vs. forest), experence (vs. experience), or paradice (vs. paradise).


State-of-the-art statistical machine translation (SMT) systems use large amounts of parallel data to estimate translation models.

this can also be seen when looking at table 3: An improvement of the symbol error rate from 6.51% to 5.96% corresponds to an improvement of mapping error rate from 21.78% to 19.40%.


To summarize: Using our beam search algorithm in an iterative fashion, we are able to improve the state-of-the-art decipherment accuracy for word substitution ciphers.

given a set of symbols V eval with unigram counts N (v) for v ∈ V eval , and the total amount of running symbols the SER can be seen as a weighted form of the MER, emphasizing errors for frequent words.


Natural language processing and understanding systems such as Machine Translation, Information Extraction and Text-to-Speech are usually trained and optimized for clean data; therefore such systems would face a challenging problem with social media text.

in principal, this is similar to using random walks for semi-supervised label propagation which has been introduced in (Szummer and Jaakkola, 2002) and then used in many other applications.


The final cost associated with a pair is: Algorithm 4.2:  We used uniform interpolation, both λ 1 and λ 2 equals 1.

the same pair of nodes can be connected with many walks of various steps (hits), and the same noisy word can be connected to many other normalized words.


This is due to the probability normalization which is done according to the nodes connectivity.

we used The BLEU score (Papineni et al., 2002) to evaluate the translation accuracy with and without the normalization.


First, it does not take into account the relative frequencies of the normalization equivalences that might share different contexts.

we examine the effect of data size and the steps of the random walks on the accuracy and the coverage of the induced dictionary.


Table 1: Generated Lexicons, steps are the Random Walks maximum steps.

we end up with a lexicon that have much higher recall than the pairwise similarity approach since it explores equivalences beyond the pairwise relation.


According to the new sequence of phrase pairs and nonterminals, during decoding the rule r 3 has discontinous translation directions on both from phrase pair ph to the nonterminal X 1 and from X 1 to ph.

no calculation is needed, the first phrase pair and the last phrase pair are the same.


(2012) add new rules into the Hiero system, Carreras and Collins (2009) apply the tree adjoining grammar formalism to allow highly flexible reordering.

table 4 shows that the P.H.+lex system gains on average 0.67 BLEU points (47.04 versus 46.37).


As Lopez (2008) describes, a phrase-salad is a common phenomenon that characterizes current SMT results.

we extract gappy-phrases (of 2-tuple) from our development dataset described in Sec.


in the early period after after the known as to and also and Human more than MT and but the not only but also no not with as well as not not The gappy-phrases depend on each other, and the more phrases extracted from human-generated (machine-translated) text are found in a sentence, the more likely the sentence is human-generated (machine-translated).

utilizing such tags makes MT detection trivial.


We evaluate the sentence-level and documentlevel accuracy of our method using the Japanese dataset.

we split each of them into two even datasets and use one for development and the other for evaluation.


A question pattern may be 1-Argument, with a variable for an entity pattern, or 2-Argument, with variables for an entity pattern and a relation pattern.

and then mapping big to population and nyc to new-york.


Aid message recognizer's SVM score of the aid nucleus template.

we applied an EM-style word clustering algorithm in  to 600 million web pages and clustered 1 million nouns into 500 classes.


The location dictionary was created from the Japan Post code data 4 and Wikipedia, containing 2.7 million location names including cities, schools and other facilities (Kazama et al., 2013).

most problem reports and corresponding aid messages were not successfully exchanged between victims and local governments or humanitarian organizations, overwhelmed by the vast amount of information.


In relation SameAlt we can mostly observe phrases that tend to introduce a new aspect, since an explicit mention of it is provided (e.g the location is, the room was) and more specific phrases like in addition are used to introduce a new aspect with the same sentiment.

table 3 presents the first analysis conducted on the full set of EDUs.


The poor performance on precision further confirms the error propagation problem in the pipeline approaches.

table 1 shows some statistics of the corpus.


Lexical features included query ngrams (up to 3grams) associated with every node in a dialog tree with removed stopwords and stemming query unigrams.

in Table 1 we report classification results for 2 baselines: a majority class and heuristicbased approach, and 2 models with different feature types: ngrams and ngrams + stems.


For this research, we used discussion posts from Volconvo.com.

existing studies are typically qualitative and focus on theorizing the socio-linguistic aspects of tolerance (more details in §2).


they focus on the problem of evaluation and recommendations on how to achieve consistent reproducible results.


And here were forests ancient as the hills, 11.

these metrics looked at segmentation as a binary classification problem and were very harsh in their comparisons-no credit was awarded for nearly missing a boundary.


In the next section, we will analyze some of these biases.

to identify these biases, we went through the annotations and came up with possible hypotheses, which we tried to verify through statistical analysis of the data.


We picked, uniformly at random, queries that are 4 to 8 words long, have only English letters and numerals, and a high click entropy because "a query with a larger click entropy value is more likely to be an informational or ambiguous query" (Dou et al., 2008).

bias 1: During flat segmentation, annotators prefer dividing the query into two segments of roughly equal length.


Furthermore, high values of α for QRand despite the very strong biases in annotation shows that there perhaps is very little choice that the annotators have while segmenting randomly generated queries.

here, we report the most prominent biases that were thus discovered.


They note that all the metrics assume that a fixed set of labels are used for items.

we adapt the equations presented in pp.565-566 of Artstein and Poesio (2008) for measuring these quantities for queries: is a distance metric for the agreement between annotations q m and q ′ n .


We automatically labeled all the prepositions in the flat annotations and classified them according to the criterion of whether a boundary was placed immediately before or after it, or on both sides or neither side.

we programmatically generate all possible trees for q, which is again dependent only on |q| and compute d 1 and d 2 between all pairs of trees, from which the expected distributions can be readily estimated.


In the Community QA sites, other users can express their opinions or evaluations on the answer.

all the answers should be relevant to the question.


If two users show same preference towards the target answer, they will have the user-preference relationship.


We formulate answer selection as a semantic matching problem with a latent word-alignment structure as in (Chang et al., 2010) and conduct a series of experimental studies on leveraging recently proposed lexical semantic models.

a recurrent neural network language model (Mikolov et al., 2010) aims to estimate the probability of observing a word given its preceding context.


In addition to following the directions suggested by the error analysis presented in Sec.

an "ideal" word alignment structure should not link words in this clause to those in the question.


In this experiment, we rank the used syntactic patterns mentioned in Section 3.1.1 according to the quantities of the extracted alignment links by these patterns.

with the rapid development of web 2.0, huge amount of user reviews are springing up on the web.


In the first component, we respectively use syntactic patterns and unsupervised word alignment model (WAM) to capture opinion relations.

then the conditional probabilities between potential opinion target w t and potential opinion word w o can be estimated by using maximum likelihood estimation.


Especially compared PSWAM with LIU, both of which are based on word alignment model, we can see PSWAM identifies opinion relations by performing WAM under partial supervision, which can effectively improve the precision when dealing with small and medium corpus.

only using nearest neighbor rule to mine the modifier for each candidate cannot obtain precise results.


2) Towards the optimal alignment under the constraints.

the sentences in online reviews usually have informal writing styles including grammar mistakes, typos, improper punctuation etc., which make parsing prone to generate mistakes.


However, because the hard constraints in §2.3 are defined based on the assumption that all the variables are binary integers, those constraints are not as meaningful when considered for real numbers.

interestingly, we observe that among the relatively frequently used English words, there are overwhelmingly more positively connotative words than negative ones.


They induce positive and negative polarities in isolation via separate graphs.

efficiency One practical problem with ILP is efficiency and scalability.


These configurations may be grouped into equivalence classes based on the number of non-zero entries.

• A binary indicator for each four letter word prefix and fertility.


Recent work (Zhao and Gildea, 2010) described an extension to the HMM with a fertility model, using MCMC techniques for parameter estimation.

the resulting model leads to substantial improvements in alignment quality.


Previous research shows that uncertainty identification is domain dependent as the usage of hedge cues varies widely in different domains (Morante and Sporleder, 2012).

given a collection of tweets T = {t 1 , t 2 , t 3 ...t n }, the annotation task is to label each tweet t i as either uncertain or certain.


(2008) constructed the BioSocpe corpus, which consists of medical and biological texts annotated for negation, uncertainty and their linguistic scope.

we just report the result based on the combination of them as n-gram features.


Because these are translations, we know that there are equivalent predicates and arguments in each pair, and that any lexical variation preserves meaning.

while our system is fully supervised, and thus dependent on manually annotated examples, we observed here that this requirement may be relatively modest, especially for in-domain data.


The stop list has only a small effect on performance except in the temporal class.

in some cases, discourse relations contain an explicit marker such as but or because which makes it easy to identify the relation.


3 Word Pairs While Marcu and Echihabi (2002)'s approach of training a classifier from an unannotated corpus provides a relatively large amount of training data, this data does not consist of true implicit relations.

this is reasonable given the intuition that the marker pattern is unambiguous and points at a particular relation.


Table 4 shows a re-evaluation of the crossdomain experiments from Table 1 for Reconcile with the LexSet features added.

pre-  vious work has evaluated the benefit of lexical features only for broad-coverage data sets.


When deciding the overall order for either ranking or classification model we used three search strategies: greedy, genetic and exhaustive (or brutal) algorithms.

we build the training examples for svm rank as follows: For a training query, which is a paragraph with n sequential sentences as 1 2 ... n s s s , we  where S is the set of all sentences in a paragraph and S is the number of sentences in S .


Nahnsen (2009) employed features which were based on discourse entities, shallow syntactic analysis, and temporal precedence relations retrieved from VerbOcean.

in this study, we investigate a more challenging and more general task of ordering a set of unordered sentences (e.g.


(2012), have already spurred numerous examples of improved empirical cross-lingual systems (Zhang et al., 2012;Gelling et al., 2012;Täckström et al., 2013).

this is likely the first reliable cross-lingual parsing evaluation.


This may also lead to modifications of the annotation scheme, which should be regarded as preliminary at this point.

this is due mainly to the fact that the the former is based on deterministic mappings from a large set of annotation schemes and therefore reduced to the granularity of the greatest common denominator.


Similarly, we can extract trigram-subtrees.


They also justified that the popular "early update" (Collins and Roark, 2004) is valid for the systems that do not exhibit spurious ambiguity 4 .

this can be efficiently implemented (without explicitly enumerating ) by checking if each LEFt(i)/RIGHt(i) in y are compatible with ( ): p i already collected all its dependents according to t; p i is attached to the correct neighbor suggested by t. For English, we use PtB as our data set.


By using sister-adjuntion to separate the ADVP node from the rest of the sentence's derivation, our model was able to use a common depth-3 elementary tree to derive the backbone of the sentence.

2 For every nonterminal node on the frontier of our derivation, we sample an elementary tree from our lexicon to substitute into this node.


In addition, our model accounts for a larger portion of the corpus with fewer rules: the top 50, 100, and 200 most common elementary trees in our model's lexicon account for a greater portion of the corpus than the corresponding sets in the TSG.

representing the argument/modifier distinction can help the learner find useful argument structures which generalize robustly.


A reverser influencer makes its object irrealis (i.e., not happen).

in 2and 3, didn't and prevented, respectively, reverse the polarity from badFor to goodFor (not killing Bill is good for Bill; preventing companies from hurting patients is good for the patients).


For this study, we want to use data that is rich in opinions and implicatures.

there could be more than one annotation in a highlighted sentence; the annotators were not told the specific number.


Table 2 lists the average accuracy with significance taken into account and then in parentheses, accuracy without taking significance into account.

ed's comments helped motivate this work.


E-rater is an automated essay scorer for standardized tests such as GMAT that also relies on a suite of NLP techniques (Burstein et al., 1998;Burstein, 2003).

this variant best balances criteria 1 and 2.


The underlying idea is to discover the most discriminant topical words in the collection and group together Web snippets containing these relevant terms.

results evidence that our proposal outperforms all state-of-the-art approaches with a maximum F b 3 = 0.452 for ODP-239 and F b 3 = 0.490 for MOrESQUE.


For instance, line 1 says when answering a when question, and the POS of current token is CD (cardinal number), it is likely (large weight) that the token is tagged as ANS.

the QA system employs a linear chain Conditional Random Field (CRF) (Lafferty et al., 2001) and tags each token as either an answer (ANS) or not (O).


The two descending lines at the bottom reflect the fact that the majority-voting mechanism from the QA system was too simple: F 1 drops as K increases.

systematic errors made by the processing tools are tolerated, in the sense that if the same preprocessing error is made on both the question and sentence, an answer may still be found.


We also thank Paul Ogilvie, James Mayfield, Paul Mc-Namee, Jason Eisner and the three anonymous reviewers for insightful comments.

predictive annotation has the limitation of being labor intensive and assuming the underlying NLP pipeline to be accurate.


Hence, these models have a greater representational power and consequently better recognition performance for in-ofvocabulary (IV) words.

the production of segmented Chinese texts is time-consuming and expensive, since hand-labeling individual words and word boundaries is very hard (Jiao et al., 2006).


Training and Evaluation We used Averaged Perceptron (Collins, 2002) (3 iterations) for training, with five-fold cross-validation.

for example, when splitting a compound noun ブ ラキッシュレッド burakisshureddo, a traditional word segmenter can easily segment this as ブ ラキッ/シュレッド "*blacki shred" since シュレッ ド shureddo "shred" is a known, frequent word.


The best performances (0.7816-0.7847) were achieved at the number of latent topics between 64 and 96.

laplacian Eigenmaps (lE) was proposed to compute corresponding natural low-dimensional structure.


The speaker maps the foreign words to a sequence of phones of his/her native language while pronouncing that foreign word.

algorithm for Epsilon Scattering : /*Initialize prob(G, P ) the probability of G matching P */ 1. for each word i in training set count with string alignment all possible G/P association for all possible epsilon positions in the phonetic transcription /* EM loop */ 2. for each word i in training set alignment path = argmax compute prob new (G, P ) on alignment path 3. if(prob = prob new ) go to 2 The second model was the EW-TP mapping.


We finally define the symmetry S as the weighted sum of S n and S e with parameter α (empirically tuned to 0.8 in our experiment).

we build two entire graphs and compute R ∞ .


Overall Average KS 0.252 ± 0.112 CKS 0.249 ± 0.033 LSOM 0.278 ± 0.086 LSM(300) 0.298 ± 0.077 LSM(600) 0.359 ± 0.062 Table 1: Average accuracy over all language pairs Π K indicates the set of all possible matrices storing one-to-one correspondences.

they then apply multivariate analysis to acquire the translingual projection.


When each document in the source domain has a class label yn, we can train a classifier on the training data set {sn, yn} N n=1.

also, we have M documents in the target domain.


However, we can not measure similarity between the topic proportions because we do not have any language resources such as dictionary.

each document is represented as a bag-of-words vector.


we can not measure similarity between the topic proportions because we do not have any language resources such as dictionary.


There are critical weaknesses of these methods when we attempt to apply them to real world cross-language NLP applications.

but that is not convex function, we only obtained local optimal.


Consumers rely increasingly on user-generated online reviews to make purchase decisions.

the rooms were clean, the concierge and reception staff were both friendly and helpful...we will definitely visit this Sheraton again when we stay in Chicago next time.


Paraphrases, in contrast, may be defined over all possible groundings.

lattices cannot compactly represent alternate word orders, a common occurrence in linguistic descriptions.


It is important to emphasize here that, while we do use Wordnet in our work, our approach does not crucially rely on it, and we use it to obtain only very shallow information.

this assumption is expressed in φ 2 (Z) as: where senses(z) is the number of different senses that attribute z has.


In Section 2, we give a short overview of theoretical background and related work on humor generation.

we used a multi-dimensional measure of humorous effect (in terms of funniness and agreement) to measure subtly different aspects of the humorous response.


If this is not the case, then the text is likely to make little sense.

the Collective Funniness of messages increases, on average, from 2.29 under condition FORM to 2.98 when the taboo constraint is added (FORM+tABOO), and further to 3.20 when the contextual constraints are added (FORM+tABOO+CONt) ( table 2).


Several other studies report results only on light verb constructions formed with certain light verbs (Stevenson et al., 2004;Tan et al., 2006;Tu and Roth, 2011).

when the noun had a determiner in the candidate LVC, it was also encoded as another syntactic feature.


(2006) focused only on true light verb constructions while only object-verb pairs are considered in other studies (Stevenson et al., 2004;Tan et al., 2006;Cook et al., 2007;Bannard, 2007;Tu and Roth, 2011).

a multilingual approach may be also beneficial in the case of monolingual applications as well.


It was interesting to see the agreement results distinguishing the top three systems against the rest, i.e.

the organisers also provided participants with access to the following additional resources: • 1 million sentences from the English-Russian parallel corpus released by Yandex (the same as used in WMt13) 5 ; • 119 thousand sentences from the English-Russian parallel corpus from the tAUS Data Repository.


Since then the MT paradigms changed many times, many systems for this language pair appeared (and disappeared), but as far as we know there was no systematic quantitative evaluation of a range of systems, analogous to DARPA'94 (White et al., 1994) and later evaluation campaigns.

the agreement results within the top three systems are low: κ = 0.23, α = 0.33, which is again in line with the results for similar evaluations between closely performing systems (Callison-Burch et al., 2011).


Universal Word Dictionary captures exact relation between these concepts by applying restrictions [chacha] uncle(icl>brother (mod>father)) and [mama] uncle(icl>brother (mod>mother)).

this makes them suitable candidates for interlingua.


The above studies rely on the high coverage of the original bilingual knowledge and a specific data source together with the translation vocabularies, co-occurrence information and language links.

(2010) took advantage of the translation equivalents inserted in Wikipedia by means of interlanguage links to extract similar articles.


Finally, similarities between templates are computed using the expanded vectors, resulting in a 'smoothed' version of the original similarity measure.

it is also interesting to compare the expansions performed by DiRT-LE-Lin-2 to the above.


For example, 'The game develop eye-hand coordination The game launch eye-hand coordination' is a rule application in this dataset of the rule 'X develop Y → X launch Y', labeled as incorrect, and 'Captain Cook sail to Australia → Captain Cook depart for Australia' is a rule application of the rule 'X sail to Y → X depart for Y', labeled as correct.

while the original DIRT algorithm utilizes the Lin measure, one can replace it with any other vector similarity measure.


As it is shown in Figure 2, Linked Data relations are often heterogeneous.

the method is unsupervised; completely data-driven requiring no apriori domain knowledge; and also language independent.


Let t denote a 3-tuple (triple) consisting of a subject (t s ), predicate (t p ) and object (t o ).

finding equivalent relations to improve coverage is important.


Overall, the relative positions of the equivalent characters in these two lists are comparable (Figure 2).

in a logarithmic scale, it is a straight line with about 45 • angle (Gelbukh and Sidorov, 2001).


We adopted the projection method based on word alignments since it is less affected by language differences.

another disadvantage of the projection method is that the accuracy of projected labels is badly affected by non-literate translation and word alignment errors, making the data contain many noises.


It is easier to obtain similar amount of parallel sentences between English and minor languages, making the conclusions more general for problems of projection in real applications.

these methods confronted the difficulty of syntactic differences between languages especially when the pair of languages varies greatly.


We simulated the full-scale retrieval approach by Jehl et al.

we alternate between the tasks of translation-based retrieval of target sentences, and the task of SMT, by re-training the SMT model on the data that were retrieved in the previous step.


It took 14 days to run 5.5M Arabic queries on 3.7M English documents.

this returns one pair of parallel twitter messages per query, which are then used as additional training data for the SMt model in each iteration.


let Ψ be the two-dimensional projection of a human body skeleton, seen by the front.


Sign languages (SL), the vernaculars of deaf people, are complete, rich, standalone communication systems which have evolved in parallel with oral languages (Valli and Lucas, 2000).

our primitive sets, were intentionally defined in a very general fashion due to the same reason: all of the perceived directions, articulators and places of articulation can easily change their domains, depending on the SL we are modeling or the technological constraints we have to deal with.


The low-quality bilingual data degrades the quality of word alignment and leads to the incorrect phrase pairs, which will hurt the translation performance of phrase-based SMT systems (Koehn et al., 2003;Och and Ney, 2004).

in (Goutte et al., 2012), they evaluated phrasebased SMT systems trained on parallel data with different proportions of synthetic noisy data.


To the best of our knowledge, our work on predicting TDI is the first of its kind.

we tried using different kernels using default parameters.


This is based on the assumption that more difficult sentences will require more time to translate.

we obtained TDIs for a set of sentences from the Translation Process Research Database (TPR 1.0) .


After normalization, the SC score for the example given becomes 15/7 = 2.14 To validate that translation difficulty depends on the above mentioned linguistic features, we tried to find out the correlation coefficients between each feature and empirical TDI.

thanks to Barto Mesa and Khristina for helping with Spanish and Danish dataset corrections.


Taking the target constituent span covering "der Produkte" as an example, the source phrase under a consistent word alignment is "of the products".

note that our definition of the gold-standard allows source-side labels to integrate bilingual information.


As opposed to the search problem, we propose a graph-based compact representation that encodes multiple alignments for machine translation.

2 When extracting rules from hypergraphs, we set the pruning threshold t = 0.5.


Finally, the affix distribution will be added to the previous stem-granularity rules.

figure 1: Translation rule extraction from Uyghur to Chinese.


so we only use RsT rules to guide the reordering.


We argue that an SMT system tuned against an adequacy-oriented metric that correlates well with human adequacy judgement produces more adequate translations.

for the newswire experiment, we used a collection of NIST 02-06 test sets as our development set and NIST 08 test set for evaluation.


We argue that by preserving the meaning of the translations as captured by semantic frames right in the training process, an MT system is constrained to make more accurate choices of both lexical and reordering rules.

each evaluation set is ranked by two evaluators for measuring inter-evaluator agreement.


Obviously, lexical cohesion devices are frequently used in real-world texts.

in the collocation, we focus on the synonym/nearsynonym and super-subordinate semantic relations 1 .


Although it is possible to have multiple lexicalizations in a partially lexicalized rule (which will exponentially increase the total number of rules), we observe that most of the time reordering is triggered by a single constituent.

as opposed to the general syntax-based reordering approaches, this strategy achieves a desired balance between broad rule coverage and specific rule match: when a fully lexicalized rule matches, it has strong influence on the permutation decision given the richer context.


This shows that although names may have monolingual origin, there are hidden factors which can allow our model to succeed, possibly related to gender or convention.

we use z = (z 1 , ..., z I ), z i ∈ {1, ..., K} to indicate the cluster of each transliteration pair x i in the training set and θ = (θ 1 , ..., θ K ) to represent the parameters of the component associated with each cluster.


If the source-target phrase pair are connected through more than one pivot phrase, we take the union of the alignments.

based on these two models, we induce a new Persian-Arabic translation model.


The reason for this behavior is that the rules that were extracted from the semantic role labeled corpus could have isolated semantic roles in them which would not necessarily get connected to the right predicate or argument during decoding.

this approach uses the semantic role labels to extract a set of special translation rules, that on the target side form the smallest tree fragments in which one predicate and all of its core arguments are present.


Formally, given an input question Q, a typical factoid QA system generates answers on the basis of the following three procedures: (1) Question Understanding, which determines the answer type and identifies necessory information contained in Q, such as question focus and lexical answer type (LAT).

and Web, and significant improvements are achieved on all data sets.


Question answering systems have been developed for many languages, but most resources were created for English, which can be a problem when developing a system in another language such as French.

(Li and Roth, 2002) trained the SNoW hierarchical classifier for question classification, with a 50 classes fine grained hierarchy, and a coarse grained one of 6 classes.


He proposes that the information content of a term t is given by the selfinformation measure − log p(t).

a case where semantic content has a direct correspondence with a lexical relation is hyponymy.


1 In Section 2, we defined semantic content as a notion encompassing various referential properties, including a basic concept of extension in cases where it is applicable.

for our experiments, we only keep the first letter of each part-of-speech tag, thus obtaining broad categories such as N or V. furthermore, we only retain words in the following categories: nouns, verbs, adjectives and adverbs (punctuation is ignored).


Given its very high precision, we decided to use this module as an initial filter, and employ the others for classifying the "harder" cases.

these results may indicate the prospects of using faceted entailment for complete entailment recognition, suggesting it as an attractive research direction.


The dataset consists of user commentaries on Egyptian news articles.

we propose a supervised approach for identifying whether a given sentence is prevalently MSA or Egyptian DA (EDA).


The authors gratefully acknowledge the support of the Defense Advanced Research Projects Agency (DARPA) Machine Reading Program under Air Force Research Laboratory (AFRL) prime contract no.

we consider two of the tasks described in (Chen and Mooney, 2008) (1) Semantic Parsing requires generating the correct logical form given an input sentence.


This situation is depicted in the following example, in which one of the domain-independent labels is omitted.

we add an intermediate layer capturing shallow semantic relations between the input sentence constituents.


For example, Hamlet's sentiment rankings upon the conclusion of his play are shown in Figure 1.

figure 3 shows Othello vs. Desdemona sentiment dynamics.


Not surprisingly, Claudius draws the most negative sentiment from Hamlet, receiving a score of -27.

as expected, the two exhibit rapidly increasing positive sentiment for each other that only tapers when the play takes a tragic course in the latter half.


As is mentioned in the first section, finding a unitary operator to describe the evolutionary process is the vital step in building a QC.

in this classification task, we selected the features by calculating the score of each term from the "earnings" category (Manning and Schütze, 2002).


Previous studies focus on combining quantum method with existing classification models such as neural network (Chen et al., 2008) and kernel function (Nasios and Bors, 2007) aiming to improve existing models to work faster and more efficiently.

the most significant factor for QC is the quality of the training samples rather than the quantity.


(Weston et al., 2012) take it further by applying this idea to deeplearning architectures.

some methods take days or weeks to learn good embeddings, and some are notoriously difficult to train.


For classifiers, we used Multinomial Naive Bayes, Bernoulli Naive Bayes (for binary counts), and Support Vector Machines.

we run baseline experiments on the dataset to establish a benchmark.


The average book got almost 30 reviews with the median being 6.

there has been little work on sentiment analysis in Arabic (Abbasi et al., 2008;Abdul-Mageed et al., 2011;Abdul-Mageed and Diab, 2012b;Korayem et al., 2012), and very few, considerably small-sized, datasets to work with (Rushdi-Saleh et al., 2011b;Rushdi-Saleh et al., 2011a;Abdul-Mageed and Diab, 2012a;Elarnaoty et al., 2012).


Unfortunately, Burger's corpus does not include Russian and Spanish data on the same scale as English.

5 The bootstrapping process terminates when there are no more new terms meeting the criteria to add.


Then, for every term not in L B(i−1) that has a frequency ≥ θ f req , the probability of that term being subjective is calculated as shown in Algorithm 1 line 10.

the lexical resources that dictionary-based methods need, do not yet exist for the majority of languages in social media.


Although bootstrapping has been used for learning sentiment lexicons in other domains (Turney and Littman, 2002;Banea et al., 2008), it has not yet been applied to learning sentiment lexicons for microblogs.

8 in the real world terms may sometimes have both usages.


These approaches assume that each document has a single source (the document's author), whose communicative goal is to evaluate a well-defined target, such as a product or a movie.

our solution is to instead define position statements, which are Abortion: Women should have the right to choose an abortion.


Previous work Balahur et al., 2010) has addressed this by giving each expression of opinion a textually-anchored target.

this does not hold in news articles, where the goal of the journalist is to present the viewpoints of potentially many people.


A lot of subsequent work focused on feature engineering that aims to find a set of effective features based on the BOW representation.

we refer to them as "opposite training set" and "opposite test set", respectively.


We adopt Google Translate 1 for review translation.

we need to find new approaches to improve the above methods.


Finally, the desired result of the task of extracting hypernym relations from text (as for any semantic relationships in general) depends on the domain and the specific later application.

given an input sentence: 1. we extract all the nouns (POS-tagging), 2. we extract all the syntactic dependencies of the nouns (dependency parsing), 3. we fed each noun (i.e., its instance) to the xmodel and to the y model, 4. we check if there exist at least one noun classified as x and one noun classified as y: in this case, we classify the sentences as definitional.


The Marathi word 'mulagaa' is linked to the Hindi word 'ladakaa' which is its exact lexical substitution.

verb disambiguation has proved to be extremely difficult (Jean, 2004), because of high degree of polysemy (Khapra et al., 2010), too fine grained senses, absence of deep verb hierarchy and low inter annotator agreement in verb sense annotation.


In this example, the word (leaf/playing card) is the subject of the verb х (to play) while Ú (tree) is not even in the same clause with (leaf/playing cards).

in this sentence, the target word 'table' will be tagged as 'the tabular array' sense since it is dominant in the chemistry domain by their algorithm.


We assume that each word in the context influences the sense of the target word independently.

verb disambiguation is very important for NLP applications like MT and IR.


This further increases the annotation costs because different datasets are needed for different tasks.

aL provides methods to select informative data points from a large pool which, if labelled, can potentially improve the performance of a machine learning algorithm (Settles, 2010).


Arabic OCR is a challenging problem due to Arabic's connected letter forms, consonantal diacritics and rich morphology (Habash, 2010).

coordinate Ascent (cA) uses a listwise linear model whose weights are learned by a coordinate ascent method to optimize a ranking metric (Metzler and Bruce croft, 2007).


In modeling contextual 4 RankSVM has also been shown to be a good choice for the meta-learner in general stacking ensemble learning (Tang et al., 2010).

only a few OCR systems have been developed (Märgner and Abed, 2009).


The output of the algorithm is a series of timelines summarization I = {I t } t=T t=1 where I t ⊂ C t Our EHDP model is illustrated in Figure 2.

(3) ET S is the timeline summarization approach developed by Yan et al., (2011a), which is a graph based approach with optimized global and local biased summarization.


As we can see, EHDP achieves better results than the two standard HDP baselines where time information is not adequately considered.

their approaches just treat timeline generation as a sentence ranking or optimization problem and seldom explore the topic information lied in the corpus.


We collected 47 gold standard documents (surveys and tutorials) on seven topics in Natural Language Processing and extracted factoids for each topic.

, 1998), and semi-supervised sense disambiguation (Yarowsky, 1995).


The SD representation proved useful in a range of downstream tasks, including Textual Entailments (Dagan et al., 2006) and BioNLP (Fundel and Zimmer., 2007), and in recent years SD structures have also become a defacto standard for parser evaluation in English (de Marneffe and Manning, 2008a;Cer et al., 2010;Nivre et al., 2010).

collapsed U-SD structures interpret func elements in order to refine the representation of relations between content words.


An available treebank is a major resource for syntactic parsing.

ure 3 shows the performance curve with iteration ranging from 1 to 10.


As in previous work, we use a Goodman-transformed TAG as our proposal distribution (Goodman, 2003) that incorporates additional CFG rules to account for the possibility of backing off to the infinite base distribution P aux 0 , and use the parsing algorithm described by Shieber et al.

we present a Bayesian nonparametric model for estimating a probabilistic TAG from a parsed corpus, along with novel block sampling methods and approximation transformations for TAG that allow efficient parsing.


In Type 1, we keep morphological information in noun categories and in Type 2, we don't.

in section 3, we develop a supertagger using the CCGbank and explore different ways of providing CCG categories from the supertagger as features to Malt.


We modified the original dependency tree and treat the comma ',' as a conjunction.

there has been a significant amount of work on Hindi dependency parsing in the recent past (Husain, 2009;Bharati et al., 2012).


Later models therefore take advantage of information from earlier models, and have wider coverage.

adopting a universal tagset avoids the need to map between a variety of different, languagespecific tagsets.


while keeping a uniform tag distribution for unrelated nodes.

many widelyspoken languages -including Bengali, Javanese, and Lahnda -have little data manually labelled for POS, limiting supervised approaches to POS tagging for these languages.


Hence the parameter associated with feature x 3 would never be updated, although this feature is also correlated with class.

we have more parameters with average weights in our models.


Although we are only examining the 13.7% of temporal relations that are co-ordinated with a signal, it is important to note the performance of conventional classification approaches on this subset of temporal relations.

this was measured over event-event relations, using all features.


The authors are grateful to the Swiss National Science Foundation for its financial support through the IM2 NCCR on Interactive Multimodal Information Management (see www.im2.ch).

section 4 presents the data and tasks for comparing sets of keywords.


One advantage of a joint approach is that one can predict portions of the complicated predicate that is buried in a table.

state-of-the-art approaches from the literature (see Figure 7).


In fact, our system corrects the relation labels of the above 6 sentences before training the relation extractor.

the sentential precision and recall is computed on a randomly sampled set of sentences from S e ∪ S F , in which each sentence is manually labeled whether it expresses any relation in R. Figure 3 shows the precision/recall curves for MULtIR with and without pseudo-relevance feedback computed on the test dataset of 1000 sentence used by Hoffmann et al.


We are also indebted to Min Zhang and Haizhou Li who provided us with the NEWS 2009 English-Chinese datasets.

we considered the NEwS 2009 English-to-Chinese transliteration task for investigating analogical learning, a holistic approach that does not rely on an alignment or segmentation model.


To avoid this data sparsity problem, all features are defined implicitly with respect to the state.

token-based paraphrases that are not covered by WordNet, such as program and software, business and venture.


We also added special features for corner cases where the current word starts or ends the source sentence, or both the previous and current words are deleted (a transition from NULL to NULL).

in the alignment model, it does not make sense to design features based on a hard-coded state, say, a feature of "source word lemma matching target word lemma" fires for state index 6.


We have collected the learner writings on the site, and released the dataset for research purpose 4 .

the target verbs are limited to the 500 most common verbs in the CLC-FCE corpus 5 .


Candidate sets for verb selection are often extracted from thesauri and/or round-trip translations.

all error tagged parts with nested correction or multi-word expressions are excluded.


While incorrect anticipation might not be problematic for native speakers, who can quickly re-adjust, non-native speakers may find inaccurate anticipation more troublesome.

we also consider the label of the closest common ancestor for a word and its next word.


This skewdness is a consequence of the kind of text in the corpus: e.g.

if we assign senses by majority voting, we need a backoff strategy in case of ties.


Also the parameters in their model were inferred by Gibbs sampling whereas we avoided this inference step by using relaxation.

(2007) which classifies each corpus occurrence of a verb as a member of one of the 168 SCFs on the basis of grammatical relations identified by the RASP (Briscoe et al., 2006) parser.


Such phenomenon typically occurs when the complexity of gold units is high enough to induce low agreement in workers' answers.

this has massively happened in our experiments and is one of the main causes of the overall cost decrease and time increase.


More interestingly, our approach outperforms MP11, showing that the role of disambiguation is more important than that of feature weighting.

we test our approach on two specialized French-English comparable corpora (financial and medical) and report improved results compared to two state-of-the-art approaches.


Using the top two words (WN-T 2 ) in context vectors increases the F-measure from 0.172 to 0.310.

these measures include three path-based semantic similarity measures denoted PAtH,WUP (Wu and Palmer, 1994) and LEA-COCK (Leacock and Chodorow, 1998).


POS features differentiate between aligned words (words in T 1 that are aligned to one or more words in T 2 ) and alignments (the edges connecting words in T 1 and T 2 ).

second, in IBM model 4, the larger distortion limit makes it possible to align distant words.


First, the typical one, in which the CLTE model is trained on labeled data for the same pair of languages L 1 -L 2 of the test set.

instead of checking what of a given sentence can be found in the other, current approaches limit the analysis to the amount of lexical elements they share, under the rough assumption that the more the better.


The overall quality of the DM.HR depends on the accuracy of extracted tuples, which is affected by all preprocessing steps.

in our experiments, ties occur when vector similarities are zero for all word pairs (due to vector sparsity).


We present a pilot evaluation DM.HR on a standard task from distributional semantics, namely synonym choice.

to make predictions, we compute pairwise cosine similarities of the target word vectors with the four candidates and predict the candidate(s) with maximal similarity (note that there may be ties).


On the other hand, significant advances in sentence-level event extraction have been made over the last decade, in particular as the result of standardization efforts such as TimeML (Pustejovsky et al., 2003a) and TimeBank (Pustejovsky et al., 2003b), as well as dedicated evaluation tasks (ACE, 2005;Verhagen et al., 2007;Verhagen et al., 2010).

in TDT, an event is defined as something happening in a certain place at a certain time (Yang et al., 1999), while a topic is defined as a set of news stories related by some seminal real-world event (Allan, 2002).


In the second experiment we focus on the task of distinguishing between news stories that describe topically very similar, yet distinct events.

a conormal product may compensate for omitted temporal relations in the input graphs.


The rationale behind the n-gram module is based on the understanding that terms appearing in non-named entity contexts are likely to be nonreferential, and terms that can be non-referential are ambiguous.

polysemy is a known problem for many NLprelated applications.


For each term, two annotators were given the term, the corresponding topic domain, and 10 randomly selected tweets containing the term.

an ontology may list a term as ambiguous if there are several potential named entities it could refer to, even if the vast majority of references were to only a single entity.


For instance, "Ventura Pons" is a "FilmDirector" and a "Person".

this paper dealt with the problem of improving the accuracy of DS.


Then we retain the top k tags S tags = {t 1 , • • • , t k } with the highest probabilities to account for an accumulated mass z: In the experiments we set z = 0.9.

we propose a component of entity type detection to check it.


's approach is a supervised method that trains a stance classifier for determining whether the stance expressed in a debate post is for or against the topic.

we find no co-relation between MAR and the remaining domains.


Recall that between two adjacent posts in a post sequence that have opposing stances, there exists a rebuttal link.

after optimization, y i 's stance is for if its x i is set to 1.


The words at the bottom are some of the remaining characteristic words together with their positions on the top-120 list.

these boldfaced words often occur as hop-based school-ofthought words.


We map the distribution over relations to a distribution over dates by defining P DateSentence (t|DS u ): We refer to this model as the Date Classifier.

there can be no universal logical constraints on the order of cancer events.


We study these questions using imperatively defined factor graphs (IDFs, McCallum et al.

as an example, the sentence "I like the low weight of the camera."


A factor Ψ i computes a scalar value over the subset of variables x i and y i that are neighbors of Ψ i in the graph.

the results with perfect knowledge of the counterpart entity show (in both directions) that the entities influence each other positively.


There are several ways to define the word importance, and we choose normalized TF-IDF (Jones, 1972).

we call such phenomenon as "sentiment matching".


For simplicity, every ⃗ f i is normalized to a unit vector, such that features of different documents are relatively comparable.


We obtained 1196 reviews from this domain.

for the example in figure 2, the polarity value of camera would be Where is the final polarity for camera and is the polarity of the word ϵ {camera, body, display, design, picture}.


15 We also experimented with native language classification on scientific documents using a version of the ACL Anthology Network (Radev et al., 2009, AAN) annotated for experiments in stylemetric tasks, including a native/non-native author judgment (Bergsma et al., 2012).

manual and automatic approaches to inducing tree fragments have recently been found to be useful in an explicit approach to text classification, which employs specific tree fragments as features in standard classifiers (Post, 2011;Wong and Dras, 2011;Swanson and Charniak, 2012).


This paper applies a phonotactic segmentation model to Korean.

conversely, a lenis-/h/ sequence indicates a word boundary, because within a word a lenis stop merges with following /h/ to become an aspirated stop.


We investigated the effect of word surprisal on the EEG signal during sentence reading.

each trained model estimated a surprisal value for each word of the 205 sentences (1931 word tokens) for which eye-tracking data are available in the UCL corpus of reading times (Frank et al., in press).


Van Petten and Luka (2012) argue that word expectations that are confirmed result in reduced N400 size, whereas expectations that are disconfirmed increase the PNP.

to prepare data for PSG training, the selected BNC sentences were parsed by the Stanford parser (Klein and Manning, 2003).


In keeping with Budanitsky and Hirst's (2006) observation that "comparison with human judgments is the ideal way to evaluate a measure of similarity or relatedness," we have undertaken the creation of a new set of relatedness norms.

pairs in the latter set were not assessed for scoring distribution validity to ensure that strongly related word pairs were not penalized by human subjects for being dissimilar.


As already discussed, one particular expert may be better at predicting the kth substructure while some other expert may be more accurate at predicting another substructure.

this leads us to considering an acyclic finite automaton G such as that of Figure 1 which admits all possible sequences of substructure hypotheses, or, more generally, a finite automaton such as that of Figure 2 which only allows a subset of these sequences.


Each of the five experts was designed to have a certain probability of making a mistake at each position in the sequence.

it was observed that the accuracy of the best model is a decreasing function of l, suggesting that ensemble algorithm may be used to improve performance for longer sequences.


Next, clients sometimes report a problem; otherwise, the agent gathers the client's identity.

by incorporating topic information, our proposed models (e.g., TM-HMMSS in Figure 5) are able to enforce the state transitions towards more frequent flow patterns, which further helps to overcome the weakness of language model.


(2012) proposed the use of latent trees that are induced during the training phase of a coreference resolver.

we follow the closed track setting where systems may only be trained on the provided training data, with the exception of the English gender and number data compiled by Bergsma and Lin (2006).


The training data is, however, not annotated with trees, but only with clusters of mentions.

our model obtains the best results to date on recent shared task data for Arabic, Chinese, and English.


The latent treeỹ needed 4 We also implement the feature mapping function Φ as a hash kernel (Bohnet, 2010) and apply averaging (Collins, 2002), though for brevity we omit this from the pseudocode.

the set of all mentions that precede m j according to the linear order including the root node, or, A j = {m i | i < j}.


Each document (talk) contains one or several keywords.

naturally, different languages express meaning in different ways.


(6) Here was {this} ir {reproachable Englishman} ready to swear in any court of law that the accused was in the house all the time.

(14) He said little about the case, but from that little we gathered that {he also was} not {dissatisfied at the course of events}.


We used the grammar together with one of its pre-packaged conditional Maximum Entropy models for parse ranking, trained on a combination of encyclopedia articles and tourism brochures.

a semantically empty word is determined to be in-scope if there is an in-scope syntax tree node in the right position relative to it, as governed by a short list of templates organized by the type of the semantically empty word (particles, complementizers, non-referential pronouns, relative pronouns, and auxiliary verbs).


Though the task-specific concept of scope of negation is not the same as the notion of quantifier and operator scope in mainstream underspecified semantics, we nonetheless find that reviewing the 2012 *SEM Shared Task annotations with reference to an explicit encoding of semantic predicate-argument structure suggests a simple and straightforward operationalization of their concept of negation scope.

as with clausal complements of clause-embedding verbs, the embedding subordinating conjunction and any other arguments it might have are inaccessible, since functor crawling is restricted to a handful of specific configurations.


These previous works include various techniques for acquiring and incorporating different kinds of linguistic and world knowledge, and further fight against the knowledge bottleneck problem, e.g.

a more complicated example is that OBJ(blame) and SUBJ(cause) can be aligned,  aligned paths are evaluated by a similarity score, for which we use distributional similarity of the words that appear in the paths ( §4.1).


When only primary knowledge is used in inference (the first row), recalls are actually very low; After we activate the on-the-fly knowledge, recalls jump to over 50%, with a moderate fall of precision.

similarly, denotation of germ OBJ(blame) in T of Figure 5 indicates the object of "blame" as in the sentence "Tropical storm Debby is blamed for death", which is a tropical storm, is Debby, etc.


Indeed, if we encounter a verb used intransitively which was only attested as transitive in the training corpus, we can simply omit the object matrix to obtain a type-appropriate representation.

symmetric composition simply sums the objects in the two tuples: vector with vector, n-th matrix with n-th matrix.


A phrase-based decoder produces its output from left to right, with each operation appending the translation of a source phrase to a growing target hypothesis.

it suffers from data sparsity and poor token-to-token correspondence with the source language.


they pre-train the RAE with an unsupervised algorithm.


Termination Check: if the joint error reaches a local minima or the iterations reach the pre-defined number (25 is used in our experiments), we terminate the training procedure, otherwise we set p s = p s , p t = p t , and go to step 2.

monotone or swap) are close to each other in the embedding space, such as the prepositional phrases.


We know from the semi-supervised phrase embedding that the learned vector representation can be well adapted to the given label.

2) we are going to learn semantic phrase embeddings with the paraphrase corpus.


In the pre-training phase (Section 3.1), we build two neural networks with the same structure but different parameters to learn a low-dimensional representation for sentences in two different languages.

the similarity scores indicate that "deliver X" and "distribute X" are more appropriate to translate the sentence.


Table 2 shows how the accuracy is improved with more features added.

they reported extensive empirical analysis and improved word alignment accuracy as well as translation quality.


For example, 发送邮件, send emails , 发送信息, send messages and 发送数据, send data .

the main reason is that parameters in the neural networks are too many to be effectively trained.


The affine form of E with respect to v and h implies that the visible variables are conditionally independent with each other given the hidden layer units, and vice versa.

h(w) and C(w) indicates whether w contains hyphen and upper case letters, respectively.


By adopting greedy layer-wise training (Hinton et al., 2006;Bengio et al., 2007), DBNs are capable of modelling higher order non-linear relations between the input, and has been demonstrated to improve performance for many computer vision tasks (Hinton et al., 2006;Bengio et al., 2007;Lee et al., 2009a).

with data preprocessing, the average accuracy boosts to about 92.02 on the test set of the target domain.


Towards identifying solutions to the problem posed in the initial post, we proposed the usage of a hitherto unexplored textual feature for the solution identification problem; that of lexical correlations between problems and solutions.

we use an IBM Model 1 translation model (Brown et al., 1990) in our technique; simplistically, such a model m may be thought of as a 2-d associative array where the value m is directly related to the probability of w 1 occuring in the problem when w 2 occurs in the solution.


The reason for such high recall is that as our ground truths are obtained from Google plus, the users from which are mostly affiliated with decent schools found in NELL dictionary.

for example, researchers obtain training data by employing workers from Amazon Mechanical Turk to manually identify users' gender from profile pictures (Ciot et al., 2013).


As a rough approximation, we check whether the changes in t 2 with respect to t 1 (which share the same topic and author) involve words or parts-of-speech that are associated with high retweet rate in a very large separate sample of unpaired tweets (retweets and replies discarded).

we refer to this alternative classifier as TAC+ff+time.


Consider a person trying to spread an important message on a social network.

if we evaluate the accuracy of the majority response among the 39 judgments per pair, the number rises to 73%.


One possible explanation of this performance gap between the reranker and our model is the small number of candidates considered by the reranker.

we demonstrate later that both types of constraints are essential.


Perhaps the most natural choice of the proposal distribution q is a conditional distribution from p. This is feasible if we restrict the proposed moves to only small changes in the current tree.

t i−x and t i+x denote the left and right context tags, and similarly for words.


To put these numbers into perspective, the bottom part of Table 3 shows the accuracy of the best systems from the 2013 SPMRL shared task on Arabic parsing using predicted information (Seddah et al., 2013).

we introduce two samplers for traversing the space of trees, Gibbs and Metropolis-Hastings with Random walk.


We do not make any assumptions about how the feature function decomposes.

table 1 summarizes all POS tag feature templates.


For each word w, we first prune out its POS candidates by using the vocabulary from the training set.

if the current parameters are θ t , and we enforce constraint Equation 5 for a particular pair y, y , then we will find θ t+1 that minimizes The updates can be calculated in closed form.


Parse extraction is then just a matter of following back pointers from the root, as in the Viterbi algorithm.

second, compared to CPUs, GPUs devote a much larger fraction of their computational power to actual arithmetic.


(2013)'s highly tuned method, but it is still fairly fast, and much simpler to implement.

we would only avoid the work of applying the rule if all threads in the warp agreed to skip it.


We compare to the Berkeley parser (Petrov and Klein, 2007) as well as two variants.

the open question is whether surface features are adequate for key effects like subcategorization, which have deep definitions but regular surface reflexes (e.g.


However, negative features-features that are not observed in any tree-are still powerful indicators of (un)grammaticality: if we have never seen a PRN that starts with "has," or a span that begins with a quotation mark and ends with a close bracket, then we would like the model to be able to place negative weights on these features.

the grammar rule 2 → 4 1 already encodes the notion of the sentiment of the right child being dominant, so when this is conjoined with our span feature on the first word (While), we end up with a feature that captures this effect.


For the count models, PMI is clearly the better weighting scheme, and SVD outperforms NMF as a dimensionality reduction technique.

does all of this even matter, or are we on the cusp of discovering radically new ways to tackle the same problems that have been approached as we just sketched in traditional distributional semantics?


No English annotators of the test set, and only one Russian annotator out of 6 participated in the selection of the training samples.

not only do we establish benchmarks for Spanish, Farsi, and Russian, but we also achieve state-of-the-art performance in English.


We further propose a method for applying our sense distribution acquisition system to the task of finding unattested senses -i.e., senses that are in the sense inventory but not attested in a given corpus.

in terms of WSD accuracy, the results over UKWAC (ERR = 0.895) are substantially higher than those for BNC, while those over TWiTTER (ERR = 0.716) are comparable.


Unsupervised word sense disambiguation (WSD) methods are an attractive approach to all-words WSD due to their non-reliance on expensive annotated data.

for each usage, we want to classify whether it is an instance of a given novel sense.


We have shown that a model that expects function words on the left periphery performs more accurate word segmentation on English, where function words do indeed typically occur on the left periphery, leaving open the question: how could a learner determine whether function words generally appear on the left or the right periphery of phrases in the language they are learning?

in order to do this it is imperative to develop better methods than the problematic "Harmonic Mean" estimator used here for calculating the evidence (i.e., the marginal probability of the data) that can handle the combination of discrete and continuous hidden structure that occur in computational linguistic models.


As we can see, characters in the first column are all Chinese number characters and characters in the second column and the third column are all Chinese family names and Chinese punctuations respectively.

we use the PKU and MSRA data provided by the second International Chinese word Segmentation Bakeoff (Emerson, 2005) to test our model.


The sentences were parsed with the Stanford parser (Klein and Manning, 2003).

in principle neural network is able to fit very complicated functions (Mitchell, 1997), and in this paper, we adapt the state-of-the-art approach described in (Socher et al., 2013) to help understand the behavior of negators specifically.


Then we perform a random walk algorithm on G tt , G oo and G to separately, to estimate all candidates' confidence, and the entries with higher confidence than a threshold are correspondingly extracted as opinion targets/words.

based on different relation types, we used three matrices to record the association weights between any two vertices, respectively.


For example, "price" and "expensive" are topically related in phone's domain, so they are a word preference for each other.

we perform experiments on real-world datasets from different languages and different domains.


It demonstrates that incorporating more prior knowledge into our algorithm would restrain other useful clues on estimating candidate confidence, and hurt the performance.

we perform extraction with a global graph co-ranking process, where error propagation can be effectively alleviated.


We encode the extracted lexical patterns along with their sentiment values as feature-label constraints.

my favorite features are the speaker phone and the radio.


PR is a framework for structured learning with constraints (Ganchev et al., 2010).

most existing machine learning approaches suffer from limitations in the modeling of complex linguistic structures across sentences and often fail to capture nonlocal contextual cues that are important for sentiment interpretation.


In general, discourse connectives can also be used to connect non-polar (neutral) sentences.

example dependency paths include nsubj(opinion, mention), nobj(opinion, mention), and amod(mention, opinion).


The lexical constraints alone are often not sufficient since their coverage is limited by the sentiment lexicon and they can only constrain sentiment locally.

most of our constraints can be factorized in the same way as factorizing the model features in the firstorder CRF model, and we can compute the expectations under q very efficiently using the forwardbackward algorithm.


W (1) is pre-trained by an autoencoder (Hinton, 1989) to capture semantic compositionality.

to alleviate the data sparsity problem, EB is first trained on a very large corpus 3 (denoted by C), and then fine-tuned on the target review corpus R. for phrasal product features, a statistic-based method in (Zhu et al., 2009) is used to detect noun phrases in R. then, an Unfolding Recursive Autoencoder (Socher et al., 2011) is trained on C to obtain embedding vectors for noun phrases.


In this set of experiments, each domain is tested by using the learned knowledge from the rest 35 domains.

we empirically set the number of clusters to the number of domains in our experiments.


At the end of inference, we calculate the posterior distributions over personas for all characters as the sampling probability of the final iteration.

with this trained model, we then select the highest-scoring antecedent within 100 words for each pronominal anaphor in our data.


Part of the motivation of our mixed effects model is to be able to tackle hypothesis class C-by factoring out the influence of a particular author on the learning of personas, we would like to be able to discriminate between characters that all have a common authorial voice.

all models are run with P ∈ {10, 25, 50, 100, 250} personas; Persona Regression additionally uses K = 25 latent topics.


To avoid features with large numerical values that dominate other features, the value of each feature is re-scaled using feature standardization approach.

we randomly sample 102 tweets for development and the remaining for evaluation.


From a systemto-system perspective, wikification has demonstrated its usefulness in a variety of applications, including coreference resolution (Ratinov and Roth, 2012) and classification (Vitale et al., 2012).

we first segment @usernames and #hashtags into regular tokens (e.g., @amandapalmer is segmented as amanda palmer and #worldwater-Day is split as world water Day) using the approach proposed by (wang et al., 2011).


The context of a single tweet usually cannot provide enough information for prominent mention detection and similarity computing for disambiguation.

to tackle these challenges, we propose a novel semi-supervised graph regularization model to incorporate both local and global evidence from multiple tweets through three fine-grained relations.


In our experimental setting, we can construct a natural Twitter network summarized by the network schema in Figure 3.

the above optimization problem can be solved directly since Q(Y) is convex (Zhu et al., 2003;Zhou et al., 2004  a closed form solution can be derived as Y * u = (D uu + µI uu − W uu ) −1 (W ul Y l + µY 0 u ).


One explanation is that since we obtained the web pages from a search engine, the most prominent entities on the web pages, such as entities in table cells in the middle of the page, are likely to be good independent of the query.

despite the simplicity, we are able to find an extraction predicate that extracts a compatible entity list in 69.7% of the development examples.


• PAGECOVERAGE (node coverage relative to the entire tree; we use depth-first traversal timestamps to estimate the fraction of nodes in the subtrees of the selected nodes.)

random words on the web page tend to have more diverse lengths and part-of-speech tags.


There are m APPEND actions, each is followed by at most (i − 1) LINK actions (line 8).

predicting mention boundaries in the joint framework raises the challenge of synchronizing different assignments in the same beam.


(PTB WSJ0036.61) Finally, to reduce the number of subject-verb agreement errors, Rajkumar and White (2010) extended the earlier model with features enabling it to make correct verb form choices in sentences involving complex coordinate constructions and with expressions such as a lot of where the correct choice is not determined solely by the head noun.

one is apt to wonder: could one use a parser to check whether the intended interpretation is easy to recover, either as an alternative or to catch additional mistakes?


A variable aligned with a verb is projected to the first S node immediately dominating that verb or, in the case of a predicative sentence, to the root of that sentence 3 .

to generate more complex output from KB data, several alternative approaches have been proposed.


It differs from the former however in that it enforces a tighter syntax/semantics integration by requiring that the elementary trees of our extracted grammar encode the appropriate linking information.

we do this only in cases where a correct grammar entry cannot be guessed from the base grammar.


To generate more complex output from KB data, several alternative approaches have been proposed.

because the extracted grammar is linguistically principled and relatively compact, it is possible to manually edit it.


The difference between our system and both Zhu and Woodsend system is significant.

(2010) and the edit history of Simple Wikipedia, Woodsend and Lapata (2011) learn a quasi synchronous grammar (Smith and Eisner, 2006) describing a loose alignment between parse trees of complex and of simple sentences.


(2009)'s studies on transition-based deep dependency parsing motivated our work very much.

readers can refer to the well-documented Perl script for details.


Intuitively, if several parsers disagree on an unlabeled sentence, it implies that the unlabeled sentence contains some difficult syntactic phenomena which are not sufficiently covered in manually labeled data.

we can see that the parser consistently achieves higher accuracy with more unlabeled data, demonstrating the effectiveness of our approach.


Previously, unlabeled data is explored to derive useful local-context features such as word clusters (Koo et al., 2008), subtree frequencies (Chen et al., 2009;Chen et al., 2013), and word co-occurrence counts (Zhou et al., 2011;Bansal and Klein, 2011).

exploiting such unlabeled data may introduce more discriminative syntactic knowledge, largely compensating labeled training data.


When a lexical resource can be viewed as a semantic graph, as with WordNet or Wikipedia, this limit can be overcome by means of alignment algorithms that exploit the network structure to determine the similarity of concept pairs.

we also show the results for this system as SB+DwSA in the table.


To ease the use of lexical neighbours in our experiments, we merged together predicates that include the same lexical unit, a posteriori.

this helps cover non classical semantic relations which are hard to evaluate with classical resources.


The JNNSE algorithm is advantageous in that it can handle partially paired data.

the results are consistent when testing on MEG data: JNNSE(MEG+text) or JNNSE(fMRI+text) outperforms NNSE(text) (see Figure 3).


Vector Space Models (VSMs) represent lexical meaning by assigning each word a point in high dimensional space.

the training and testing word sets are disjoint.


We are particularly interested in the work of Bowling and Veloso (2002) who proposed the PHC and PHC-WoLF algorithms that we use in this paper.

most approaches assume that the user goal is fixed and that the behavior of the user is rational.


A stochastic game is defined as a tuple (n, S, A 1...n , T , R 1...n , γ) where n is the number of agents, S is the set of states, A i is the set of actions available for agent i (and A is the joint ac-tion space A 1 × A 2 × ... × A n ), T is the transition function S × A × S → [0, 1] which defines a set of transition probabilities between states after taking a joint action, R i is the reward function for the ith agent S × A → , and γ is a factor that discounts future rewards.

3 Single-Agent vs. Multi-Agent Reinforcement Learning Reinforcement Learning (RL) is a machine learning technique used to learn the policy of an agent, i.e., which action the agent should perform given its current state (Sutton and Barto, 1998).


To ensure that the policies do not converge by chance, we run the training and test sessions 20 times each and we report averages.

clearly having a constant exploration rate in all epochs is problematic.


For comparison, in (English and Heeman, 2005) the state specification for each agent included 5 binary variables resulting in 32 possible states.

the two agents have different reward functions.


Imagine a system that learns to change its strategy as it realizes that a particular user is no longer a novice user, or that a user no longer cares about five star restaurants.

it is important to investigate RL approaches that do not make such assumptions about the user/environment.


First, with a  greedy bottom-up strategy, we develop a discourse parser with a time complexity linear in the total number of sentences in the document.

for each sentence S k with m k EDUs, the overall time complexity to perform intra-sentential parsing is O(m 2 k ).


In this paper, we propose a graph model to represent the contextual discourse information from both lexical and topic perspectives.

 Topic Modeler: For estimating transition probability Pt(i,m), we employ GibbsLDA++ 6 , an LDA model using Gibbs Sampling technique for parameter estimation and inference.


in (Li et al., 2005), new word detection was viewed as a binary classification problem.


More advanced methods such as (Kanayama and Nasukawa, 2006) adopt domain knowledge by extracting sentiment words from the domain-specific corpus.

segments are left unlabeled if their candidate labels belong to mutually exclusive categories with the same confidence.


Each partial sentence is annotated with its semantic information, built up using the semantic annotations associated with the PLTAG trees.

the problem is restricted because in our work, we do not consider the issue of how to fragment a complex goal into multiple sentences (discourse planning).


One direction can be thought of as "overgeneration and ranking."

sTRUCT naturally handles probabilistic grammars.


Furthermore, recent work of (Frank et al., 2013), show that word surprisal predicts N400, providing concrete motivation for artificial manipulation of text to explicitly elicit word learning through natural reading, directly motivating our approach.

p (constrained(w i ) = 1|w) is a real value (scaled between 0 and 1) that represents the degree of constraint imposed on the meaning of word w i by its context.


According to our survey, compared to the same work in English, the Chinese relation extraction researches make less significant progress.

(2008) experimented with different kernel methods and inferred that simply migrating from English kernel methods can result in a bad performance in Chinese relation extraction.


The first impression is that more lexicon entries result in more power.


Based on the characteristics of Chinese, in this paper, an Omni-word feature and a soft constraint method are proposed for Chinese relation extraction.


It is trivial to validate, as we will do later in this paper, that active learning can also alleviate the annotation burden for relation extraction in one language while retaining the extraction performance.

for example, the Chinese entity mention "官员" (officer) is translated to "officer" in isolation, it is, however, translated to "officials" when in the relation instance "叙利亚 官员" (Syrian officials).


Substituting, we have except the constraint that µ = −c − λ implies that for µ to be positive λ ≤ c. Since this Lagrangian has the same form as equation 3, we can also derive a dual problem, which is the same as in equation 4, with the additional constraint that each λ i can not be bigger than its cost c i .

since we truncate penalties at 0, this suggests that we will learn a penalty of 0 for constraints in three categories: constraints that do not hold in the ground truth, constraints that hold in the ground truth but are satisfied in practice by performing inference in the base CRF model, and constraints that are satisfied in practice as a side-effect of imposing non-zero penalties on some other constraints .


The projection operator Π consists of truncating all negative coordinates of λ to 0.

this only amounts to 1.24 times as much work as running the baseline CRF on the dataset, since the constraints are satisfied immediately for many examples.


Table 1 shows how each type of constraint family improved the F1 score on the dataset.

recent work has demonstrated improvements in citation field extraction by imposing soft constraints (Chang et al., 2012).


We now discuss how to address the first challenge, i.e,.

unfortunately, such one-to-many concept mappings could hinder the retrieval performance in the following two ways.


However, this method also suffers the limitation that its effectiveness depends on the accuracy of the concept mapping results.

the Medical Records track of the text REtrieval Conference (tREC) provides a common platform to study the medical records retrieval problem and evaluate the proposed methods (Voorhees and tong, 2011;Voorhees and Hersh, 2012).


Prior knowledge of the sentiment of words, such as sentiment lexicons, has been incorporated into cross-domain sentiment classification.

the DA method proposed in Section 4.1 is shown as the Proposed method.


The sentiment of a word can vary from one domain to another.

we do not impose any further restrictions on the set of pivots W other than that they occur in both domains.


The S pred and T pred baselines simulate the two alternatives of using source and target domain distributions instead of learning a PLSR model.

unlike our method, SCL, SFA, or SST do not learn a prediction model between word distributions across domains.


Second, we learn a mapping from the source domain latent feature space to the target domain latent feature space using Partial Least Square Regression (PLSR).

from Figure 1 we see that in 10 out of the 12 domain-pairs the Proposed method returns higher accuracies than SST.


This motivates the need for a frequency-driven view of lexical semantics.

while the Viterbi algorithm can be used for tagging optimal state-sequences given the weights, the structured perceptron can learn optimal model weights given gold-standard sequence labels.


(2012), that model vector composition through non-linear transformations.

we believe that ours is the first method that can invoke different meaning representations for a token depending on textual context of the sentence.


The weight corresponding to the feature where w j h,i j is the i th j element in the vector w j h .

most traditional models are linear models, in the sense that both the features of the data and model parameters are represented as vectors in a vector space.


A tensor is a multidimensional array, and is a generalization of commonly used algebraic objects such as vectors and matrices.

a true feature weight is now approximated by a set of bases.


Most of the learning algorithms for NLP problems are based on vector space models, which represent data as vectors φ ∈ R n , and try to learn feature weight vectors w ∈ R n such that a linear model y = w • φ is able to discriminate between, say, good and bad hypotheses.

it is hard to know the structure of target feature weights before learning, and it would be impractical to try every possible combination of mode sizes, therefore we choose the criterion of determining the mode sizes as minimization of the total number of parameters, namely we solve the problem: The optimal solution is reached when D is an integer, therefore we choose extra entries of the tensor correspond to no features and are used just for padding.


For the unlabeled phrases, the set of possible target translations could be extremely large (e.g., all target language n-grams).

we obtain these candidates from two sources: 2 1.


The notion of translation consensus, wherein similar sentences on the source side are encour-aged to have similar target language translations, has also been explored via a graph-based approach (Alexandrescu and Kirchhoff, 2009).

lP is suboptimal for our needs, since it is unable to appropriately handle generated translation candidates for the unlabeled phrases.


Kendall's Tau; see (Callison-Burch et al., 2012) for details.

during each cross-validation run, we trained our pairwise ranker using the human judgments corresponding to nine of the ten folds.


6 In ASIYA the metrics from this family are referred to as "Discourse Representation" metrics.

to do so, we contrast different Mt evaluation metrics with and without discourse information.


Furthermore, sentence-level scoring (i) is compatible with most translation systems, which work on a sentence-bysentence basis, (ii) could be beneficial to modern MT tuning mechanisms such as PRO (Hopkins and May, 2011) and MIRA (Watanabe et al., 2007;Chiang et al., 2008), which also work at the sentence-level, and (iii) could be used for reranking n-best lists of translation hypotheses.

dR-LEX includes the lexical items to account for lexical matching; moreover, it separates the structure (the skeleton) of the tree from its labels, i.e.


However, these earlier studies focused on the ngram translation models, where the translation probability of a phrase or a sentence is decomposed as a product of n-gram probabilities as in a standard n-gram language model.

second, we present a new learning method to train the weights in the multilayer neural network for the end-to-end BLEU metric directly.


No such sharing was possible in the original symbolic space for representing words or phrases.

we find in a pilot study that it is good enough to set it to 1 when the values of all the baseline feature weights, used in the log-linear model of (1), are properly normalized, such as by setting = / for = 1 … , where is the unnormalized weight value of the target language model.


This new paradigm for QE makes it possible to: i) let the QE system learn from one point at a time without complete re-training from scratch, ii) customize the predictions of an existing QE model with respect to a specific situation (posteditor or domain), or even iii) build a QE model from scratch when training data is not available.

each translation job has its own specificities (domain, complexity of the source text, average target quality).


A common trait of all current approaches, in fact, is the reliance on batch learning techniques, which assume a "static" nature of the world where new unseen instances that will be encountered will be similar to the training data.

as a final analysis of our results, we investigated how the performance of the different types of models (batch, adaptive, empty) relates to the distance between training and test sets.


In this task, simple concatenation of visual and textual attributes does not yield improved performance over the individual modalities (see row Attributes in Table 5).

each noun is represented as a vector with dimensions corresponding to attributes elicited by participants of the norming study.


All these models run on the same datasets/items and are given input identical to our model, namely attribute-based textual and visual representations.

we use indirect supervision in the form of object classification in addition to the objective of reconstructing the attribute-centric input representation.


Recent studies (Zhu et al., 2013; show, however, that this approach can also achieve the state-of-the-art performance with improved training procedures and the use of additional source of information as features.

the StateAlign system corrected a lot of the NP-internal errors (NP Int.).


proposed a transition-based constituent parser to process an input sentence from the character level.

for example, for the template q 0 wt , the POS tag of the first word q 0 in the queue β is required, but it is not specified yet at the present state.


First, we incorporated POS tagging into transitionbased constituent parsing to resolve the error propagation problem of the pipeline approach.

we can concisely utilize non-local features in a single-stage parsing system.


For instance, if an error in voice occurs as in *I excited this, correctly, I was excited by this, error case frames are not properly applied.

the first one was based on error case frames generated from the training set of the KJ corpus.


To verify if it really describes an erroneous preposition use, the native case frames are searched for; if it matches one of them, that means that the use of the preposition actually appears in the native  corpus.

for example, one might add a case element such as supermarket to the preposition case "Prep to:{market,store}" in the example error case frame.


The difference can be explained by the way the two smoothing methods estimate p ′ .

for our experiments, we used all three, for both integral counts and count distributions.


We applied expected KN smoothing to all iterations of all models.

chen and Goodman (1999) introduce three extensions to Kneser-Ney smoothing which are now standard.